Welcome to the Webinar
Labor Strategy Optimization for the Professional Services Industry

Speaker Introduction• Dr. Haitao Li• Professor of Supply Chain & Analytics at the University of Missouri- St. Louis• Ph.D. in Operations Management from the University of Mississippi, USA• Over ten years of research experience in optimization modeling, algorithm
design and their applications in scheduling, resource allocation and supply
chain configuration

Copyright 2018, Gurobi Optimization, Inc.

2

Speaker Introduction• Dr. Cipriano Santos• Sr. Technical Content Manager at Gurobi• Worked at Hewlett-Packard Enterprise for 23 years• Retired as Distinguished Technologist• Developed and implemented several decision support tools for Product Lifecycle Management, Customer Relationship Management, Large Data Centers
Computing Resources Allocation, Professional Services Workforce Planning,
Airline Dispatcher Workload Distribution Optimization, and Operating Room
Planning & Scheduling• Holds a Bachelor’s Degree in Applied Mathematics from the University of
Mexico (UNAM), and a Master’s and PhD degrees in Operations Researchfrom the University of Waterloo, Canada

Copyright 2018, Gurobi Optimization, Inc.

3

Agenda• Motivation and business setting of strategic workforce planning at a Professional Services firm
• Problem description and spreadsheet approach• Optimization model formulations and solutions methods• Business impact and managerial insights• Lessons learned and best practices in developing and deploying an optimization application© 2018 Gurobi Optimization

Motivation and business setting of strategic
workforce planning
Professional Services Companies© 2018 Gurobi Optimization

Workforce planning in the professional services
industry• People are the most important asset in the knowledge economy, particularly in the professional
services industry.
• Large professional services companies employ thousands of professionals to deliver a wide
variety of services (jobs), making labor the industry’s highest expense.
• Workforce resource supply-demand matching is challenging when considering thousands of
employees, with thousands of skills to be “optimally” mapped to thousands of services (jobs).
• The manual spreadsheet-driven approaches used in most companies cannot be sustained if wewant to “optimize” both the workforce and the financial growth of the industry.

© 2018 Gurobi Optimization

Workforce planning process flow

ServiceDemandattrition

Consulting
Firms

Project
Tasks

Big Data
MiningProjects
Portfolio
Process
Flow

ServiceDemandForecast

Bill
of
Labor

Contact
Centers

Calls, orTransactions

Big Data
MiningLabor
Requirements
Forecast

Sup/DemMatching:=
OptimalGap
Closing© 2018 Gurobi Optimization

Labor
Fulfillment
Plan
IT Labor
SupplyLabor
Transformation& Procurement
Plans

Big Data
MiningProblem statement• Workforce planning objectives at professional services companies• Increase (billable) utilization of workforce resources• Reduce overall labor costs• Properly match service (job) requirements with labor resources’ capabilities and availability• The fundamental problem of workforce planning is to match labor resources• with the right skills,• for the right job,
• at the right time,• at the right location,• at the right cost.

© 2018 Gurobi Optimization

Hierarchical Workforce Planning Components• Strategy model (Labor Strategy Optimization)• Given a forecast revenue of a service offering, determine budgets for these strategies to maximize total grossmargins:• location: onshore/offshore• labor mix: internal/contingent workforce, agency labor• labor transformation: training/re-skilling, hiring/layoffs, promotions/demotions• Tactical model• For the above strategy budgets, select and schedule a portfolio of projects that optimizes the trade-offs ofconflicting objectives while considering budgets, labor resources, and other constraints.
• This model determines the labor resource requirements to fill the jobs of selected projects.

• Operational model• Given the above resource requirements, determine “best” resources (by name) available to fill therequirements of jobs for the selected projects in the optimal portfolio.

• Execution• Track execution and provide feedback loops to Operational/Tactical/Strategy models.

© 2018 Gurobi Optimization

Description of the labor strategy
Professional Services Companies© 2018 Gurobi Optimization

Challenges of strategic workforce planning• Complexity of Service Transformation and Delivery• From early demand signal, to labor requirements, to allocation of labor resources, to delivery• Heterogeneous Demand of Projects/Tasks:• Skill requirements, proficiency/experience level• Heterogeneous Supply of Workforce Resources:• Skill, pay rate, productivity, organization, geographical location• Flexible Source of Labor: internal workforce, contractors, third-party partners, onshore, offshore• The need for cross-training and labor transformation• Decentralized Staffing Decision with Visibility and Accessibility of Centralized Global Resource
Pool© 2018 Gurobi Optimization

Labor strategy defined• Capacity:• Level and amount of manpower measured
in FTE (full time equivalent)

Capacity• Capability:• Capabilities and roles of each individual
Flexibility

Labor
Strategy

Capability• Location:• Onshore/offshore
Location• Workforce Flexibility:• Internal and external workforce© 2018 Gurobi Optimization

Idea behind IWF and CWF strategy
Assume demand of low priority projects, or projects requiring non-critical skills, is allocated to 3PP

FTE

FTE demand

FTE trend

CWF
CWF
CWF

Hire IWF (*)

IWF baseTime© 2018 Gurobi Optimization(*) Professional services firms
will lay-off IWF if trend slope isnegative.

Strategic workforce planning spreadsheet
approach
Professional Services Companies© 2018 Gurobi Optimization

Introduction to spreadsheet approach of former HP C&I• HP C&I offered services (market offerings) all over the world.
• There were four major regions,• NA –North America, EMEA –Europe, Middle East, and Africa, AP –Asia Pacific, and LA –Latin America.

• Market offerings included:• application solutions (APS), enterprise integration services (ENI), financial services (FS), manufacturing services(MFG), network services (NSP), etc.

• Labor strategy planning was done every six months, considering a single planning period.
• Four sources of labor were considered:• IWF, CWF, OWF (offshore workforce), and 3PP• Four roles and capabilities were considered:• PM: project manager• SA: solution architect• BC: business consultant• TC: technical consultant© 2018 Gurobi Optimization

Introduction to spreadsheet approach of former HP C&I• Input parameters of spreadsheet approach:• WDAYS: workdays during planning horizon (six months)• OWF%: percentage of revenue allocated to offshore workforce (India)• 3PP%: percentage of revenue allocated to third-party partners (Softtek)• $RATE: consulting fee/day• REVPM: revenue value per PM• CORE%: productivity of IWF – internal workforce. It is the percentage of time the FTE generates
revenue.
• CTW%: percentage of contingent workforce in the total workforce mix (IWF + CWF)
• Assume productivity of CWF is 100%

• BID%: percentage of time that workforce spends on biding• RISK%: percentage of deliverable time not billable due to project disruption• SA%: percentage of total FTE requirements needed for solutions architects• BC%: percentage of total FTE requirements needed for business consultants© 2018 Gurobi Optimization

Spreadsheet approach for labor strategic planning
Market offering
Target Revenue:$14.706M

REVPM = $3.5M/PM

OWF% = 18%
3PP% = 14%
1-OWF% - 3PP% = 68%$RATE = $10K/day$10M
1000 Billable days

NbDaysSold$2.647M

IWF &CTW
Revenue$2.058M
3PP
Revenue

OWF
Revenue[ CORE%*(1 -- CTW%) + (1 – BID)*CTW% -- RISK% ] = 0.64
1562.5 working days
Number of
FTE
neededNbDaysNeededWDAYS = 130 days/FTE

Internal
HP FTE

4.202

0.287

0.962

PM

SA

4

1
3

SA

1

1

BC

0
0

0

PM

9.62 FTE

4.369

12.02 FTE

CTW

2.4 FTEFTE Requirements

TC

4
0

GAP = FTE_Req --- FTE_Inv

BC

TC

0

6

FTE Inventory

Work Utilization
CORE% = 70%
CTW% = 20%
BID% = 10%
RISK% = 10%

SA% = 10%
BC% = 3%

Limitations of a spreadsheet-based approach• A cost-accounting approach with no optimization capability• Missed opportunity to maximize total gross margins for all market offerings• Missed opportunity to increase resource utilization by training and re-skilling• Manually adjust revenue allocation and labor sourcing decisions, in a trial-and-error fashion• Time-consuming!

© 2018 Gurobi Optimization

Optimization model formulations and solution
methods
Professional Services Companies© 2018 Gurobi Optimization

Labor strategy optimization (LSO)• A Prescriptive (Optimization) Methodology and Tool for the Strategic Level Workforce
Optimization of a PSO• LSO Provides Decision-Support for a PSO’s Workforce Decisions in Four Integrated Dimensions:• Capacity Strategy: How many FTEs are needed?
• Capability Strategy: What kind of mix of skills/roles will the firm need?
• Location Strategy: Where to source the labor?
• Flexibility Strategy: How to achieve flexibility by using external workforce?

• Data-Driven Decision-Support© 2018 Gurobi Optimization

High-level sketch of LSO
Target Revenues ofMarket Offerings

Firm-Specific Service
Transformation

Firm-Specific Cost
Structure of Labor

Available Capacity andCapability of Internal
Workforce

Risk Assessment
and Management

The LSO Models andAlgorithms

Cross-Training
Policy and Rules

Amount of Each Resource
Needed in the Service
Transformation Process

Amount of Resources CrossTrained to Other Resources

Identified Potential Gap in
Capacity and Capability© 2018 Gurobi Optimization

Modeling the key components of LSO• Bill-of-Labor (BOL)• Modeling Cross-Training• Modeling Supply Side Risk and Uncertainty© 2018 Gurobi Optimization

Introduction to Bill-of-Labor (BOL)• Analogous to the well-known Bill-of-Materials (BOM) in manufacturing• Describes the firm-dependent service transformation and delivery• A hierarchy of labor resources as inputs• The generated revenues as outputs• It includes the dependent demand of labor resources as building blocks

Key data needed for BOL• !: the discounted internal rate of an employee, i.e. the revenue generated by an internal FTE perday. ($RATE)• ": the number of working days in the planning horizon. (WDAYS)• $% : risk of an offshore resource of role & as percentage of deliverable time that is not billable due toproject disruption caused by time zone difference, lack of communication, etc. (RISK%)• '% : the commitment rate of FTE of role & as the percentage of time the FTE generates revenue. It
can be calculated as the average number of hours billed divided by the total number of hours
worked. (CORE%)• (%) : is the efficiency of training measured as percentage of time needed to transform FTE of role &to *Demand dependency modeled as a multiplier !"# (%)
Involved
Parameters-

FunctionalForm
1

Modeling revenue and FTE allocation

', (

'(

Revenue generated by one internal FTE', (, )*'((1 − )* )Revenue generated by an offshore FTE

-*

-*

.*/

1 − .*/

Interpretation

Commitment rate of an FTE
Efficiency of trainingExamples of demand dependencies in BOL
Target Revenue in
SAP

Total FTE
Requirements

11

Revenue Allocatedto Offshore

1

Revenue Allocatedto Contractors(a) Modeling revenue allocation

Project Managers
FTE Requirements

Offshore FTE
Needed(d) Modeling offshore risks

1

τ*η

Resource Managers
FTE Requirements(b) Modeling FTE composition

Revenue Allocatedto Offshore
τ*η*(1-λ)Revenue Allocatedto Onshore

Regular FTE
Headcounts(c) Modeling productivity

Program Manager ofSAP

FTE Needed1µOnshore FTE
NeededµCTW FTE
Headcounts(e) Modeling workforce efficiency

1-φBusiness Consultant
of SAP(f) Modeling workforce transformation

An example of BOL
Forecasted target
revenue

1

1

1
3PP
RevenueOnshoreRevenue

Level-1
Offshore
Revenueτη(1-λ)τηNumber of
FTE
needed

µ

1
PM

1
SA

1
BC

SA

BC

µ
CTW
FTE

1
TC

PM

TC

1
SA

1
BC

Level-3µ

Regular
FTE

1

1-φ
PM

Number of
FTE
neededµ

Regular
FTE

Level-2

CTW
FTE

1

Level-4

TC

1-φLevel-5
PM

SA

BC

TC

Modeling cross-training
Market 1

Market 2

SA

SA

BC
10%

BC

30%
TC

TC

PM

PM

SA: Solution architect
BC: Business consultant
TC: Technical consultant
PM: Program manager

Modeling supply side risk and uncertainty in BOL• Unlike the product structure in a manufacturing BOM, which is often stable and deterministic, thestructure of a BOL may involve uncertainty• This unique feature of BOL is due to the intrinsic nature of service transformation: high degree ofcustomization and the underlying learning effect• One way to model this uncertainty is to treat some parameters in the demand-dependency
function ℎ"# (%) as stochastic random parameters• For instance, the commitment rate ' of an FTE and the offshore risk factor ( might be uncertain,
but may follow various probability distribution based on historical data or expert experienceDeterministic MILP model: sets and parameters! M : set of marketing offerings! I : set of internally-owned resource/labor! O : set of outsourced resource/labor!: set of all nodes in a BOL, i.e. ! = ! O ∪ ! I ∪ ! M where set of nodes do not overlap.
': set of arcs in a BOL, representing the demand-dependency of resource/labor(: set of arcs in a BOL that are eligible for training)* : target revenue of market offering * ∈ ! M,* : cost ratio of outsourcing to external source * ∈ ! O . It is the ratio between the payment to * and therevenue generated by *.
-* : cost per day per internally-owned resource * ∈ ! I . For a regular FTE, it is the salary; for a CTW FTE,
it refers to the payment to the CTW.
.* : available inventory of internally-owned resource * ∈ ! I .
ℎ*0 (∙): the outflow multiplier of node * going into node 0 for each arc (*, 0) ∈ '. ℎ*0 (∙) is a generic form toinclude some specific functional forms that involve the commitment rate 5, risk of resource 6 and others
as special cases.
© 2018 Gurobi Optimization

Deterministic MIP model: decision variables!"# ≥ 0: FTE amount of resource used for training from resource type " to resource type # for an eligible
training pair (", #) ∈ *+" ≥ 0: idled FTE amount of internally-owned resource type " ∈ , I
I
1
if
the
idled
internally−ownedresourcetype"∈,
can be positive (+" > 0)." = 00 otherwise

1 if the training from resource " to resource # can be positive for (", #) ∈ * K!"# > 0L
H"# = 00 otherwiseDeterministic MILP model: objective function

Max%':(',+ )∈.,+ ∈/ M&' − % 1' &' − % 34' &' − % 34' 6'+ 7'+ − % 34' 9'
'∈/ O

'∈/ I

(',+ )∈8

'∈/ I

(2)

Deterministic MILP model: constraints
Subject to:∑# :(# ,()∈+ "# ≤ ,(∀( ∈ / M(3)"# = ∑(:((,# )∈+ ℎ(# (∙)"(∀# ∈ /\/ M(4)"( = 7( − ∑# :((,# )∈: 9(# + ∑# :(# ,()∈:(1 − =#( )9#( − >( ∀( ∈ / I(5)>( ≤ 7( ∙ A(∀( ∈ / I(6)

9(# ≤ 7( ∙ C(#∀((, #) ∈ :(7)

A( + C(# ≤ 1∀((, #) ∈ :(8)"( ≥ 0, 9(# ≥ 0, >( ≥ 0, A( ∈ {0,1}, C(# ∈ {0,1}(9)The two-stage stochastic programming model
Random Parameters!"# : commitment rate of a resource # ∈ % I'(# : risk of offshore operations of a resource # ∈ % Iℎ*#+ (∙): a function of random parametersILet Ω be the set of sample space, and 0 1 = {!#1 , '1:∀#∈%
} be a set of realization of the#

9 = {!"# , '(# : ∀# ∈ % I }, where 1 ∈ Ω. We assume that 0
9 follow a discreterandom parameter set 0
distribution with a finite number of scenarios, with :1 being the probability of scenario 1, i.e.
9 = 0 1 @ = :1 , and satisfying ∑1 ∈Ω :1 = 1 , :1 ≥ 0 ∀ 1 ∈ Ω . Associated with each;<=>?00 1 is weight function ℎ#+1 (∙).

Decision variables
First-Stage Decision Variables!" ≥ 0: target revenue allocated to a predecessor " of a market offering node, i.e. " ∈ &.
Second-Stage Decision Variables
For each scenario ' ∈ Ω, we define the following second-stage decision variables:)*' ≥ 0: FTE amount of resource type * which is not a market offering node or a predecessor of a market
offering node, i.e. * ∈ +\+ M \&.*"' ≥ 0: FTE amount of resource used for training from resource type * to resource type " for an eligible
training pair of (*, ") ∈ 2
3*' ≥ 0: idle FTE amount of internally-owned resource * ∈ + I'
I
5*' = 71 if the idled internally − owned resource * ∈ + can be positive at scenario ' ∈  (3* > 0)0 otherwiseO*"'= P1 if the training from resource * to resource " can be positive for0 otherwise(*, ") ∈ 2 at scenario ' ∈  (.*"' > 0)

Objective function and constraints
Max% &' − % +, - % .' /', + % 34' /', + % 34' 6'7 8'7, + % 34' ='7, >
'∈)

,∈Ω

'∈0 O

'∈0 I

(',7 )∈<

(10)

'∈0 I

Subject to:∑7 ∈)' &7 ≤ D'∀' ∈ 0 M(11)/7, = ∑':(',7 )∈J ℎ'7, (∙)/',∀7 ∈ 0\0 M \), , ∈ Ω(12)/', = M' − ∑7 :(',7 )∈< 8'7, + ∑7 :(7 ,' )∈< N1 − 6'7 O87', − =',∀' ∈ 0 I , , ∈ Ω(13)=', ≤ M' ∙ Q',∀' ∈ 0 I , , ∈ Ω(14)

8'7, ≤ M' ∙ S'7,∀(', 7) ∈ <, , ∈ Ω(15)Q', + S'7, ≤ 1∀(', 7) ∈ <, , ∈ Ω(16)/', ≥ 0, 8'7, ≥ 0, =', ≥ 0, Q', ∈ {0,1}, S'7, ∈ {0,1}(17)Business impact and managerial insights
Professional Services Companies© 2018 Gurobi Optimization

A case study
Market offering

Description

Forecasted Revenue

APS

Application solutions$347,712,130

ENIEnterprise integration services$921,068,469

FS

Financial services$58,845,227

MFG

Manufacturing services$70,821,142

NSP

Network services$330,549,579

PS

Border, trade and protection$142,005,182

RPO

Custom consulting$75,536,111

Total$1,946,537,840

The data in this Case Study includes real-business scenarios, but has beenmasked to protect proprietary information.

BOL in one-market offering
Forecasted target
revenue

1

1

1
3PP
RevenueOnshoreRevenue

Level-1
Offshore
RevenueτηλτηNumber of
FTE
needed

µ

1
PM

1
SA

1
BC

SA

BC

µ
CTW
FTE

1
TC

PM

TC

1
SA

1
BC

Level-3µ

Regular
FTE

1

1-φ
PM

Number of
FTE
neededµ

Regular
FTE

Level-2

CTW
FTE

1

Level-4

TC

1-φLevel-5
PM

SA

BC

TC

Capacity of internal workforce by market offering,role and location
OnshoreMarketOffshore

Offering

SA

BC

TC

PM

SA

BC

TC

PM

APS

34

0

124

19

41

0

149

0

ENI

24

9

413

7

29

11

496

0

FS

0

5

0

16

0

6

0

0

MFG

2

5

3

11

2

6

4

0

NSP

32

2

78

12

38

2

94

0

PS

3

7

5

14

4

8

6

0

RPO

0

2

7

5

0

2

8

0

Similarity coefficient for cross-training

Case study results

Financial Performance
OnshoreGross Margin
Offshore
3PP
Total FTE Requirements
Onshore-Regular
Onshore-CTW
Number of
FTEs
Offshore-Regular
Offshore-CTW

Spreadsheet
81%
28%
65%
7%
1681
28%
2%
66%
4%

Restricted LSO
92%
34%
59%
7%
1630
40%
0.1%
50.9%
9%

LSO
100%
34%
66%
1713
38%
0.1%
50%
11.9%

Business Impacts• The optimal LSO solution generates a higher gross margin:• 9% more than the restricted LSO• 23% more than the spreadsheet solution• The LSO solution provides an optimal labor mix, different from the other two (heuristic) solutions:• does not use 3PP• provides higher Offshore CTW percentage• Additional insights can be obtained through sensitivity analysis on some key parameters• Complete computational results are available in our paper:• Optimizing the Labor Strategy of a Professional Service Firm (2018), by H. Li, C. A.
Santos, A. Fuciec, T. Gonzalez, S. Jain, C. Marquez, C. Mejia, A. Zhang.

© 2018 Gurobi Optimization

Impact of bill rate on overall LSO solution

Impact of bill rate on cross-training

Impact of commitment rate of CTW FTE

Impact of offshore risk

Lessons learned and best practices
Professional Services Companies© 2018 Gurobi Optimization

Lesson 1:• Mathematical modeling is an iterative process• Follow a prototyping process where you:
•
•
•
•
•

Gather business requirements
Define data requirements
Build the appropriate optimization model
Build a prototype solution tool where you can show output reports and do what-if scenario analysis
Ask the decision maker: is the solution implementable?
••

If no, go back to previous steps as appropriate
If yes, transfer prototype to business until it is stable, no bugs. Then transfer to IT organization for long term firstlevel support© 2018 Gurobi Optimization

Lesson 2:• Not transferring your prototype solution to an IT organization for productization means
death of your application in the long-term.
• The systems generating data for your application will be upgraded and evolve• If your prototype solution has not been transferred to IT, your application won’t be in IT dev roadmaps• Your application won’t receive the required data, and your end user won’t be able to use it© 2018 Gurobi Optimization

Lesson 3:• Evangelize mathematical optimization at your company• Your company has many optimization problems - maximization of operational efficiency at various
business functions and business units• Identifying an optimization problem is non-trivial• Requires an optimization expert and a subject matter expert (SME)• Educate business people and top executives about the money they are leaving on the table, due to not
utilizing mathematical optimization• Organize seminars, tech fairs, advertise your success with mathematical optimization, show what is
possible when mathematical optimization is implemented© 2018 Gurobi Optimization